System and method for identifying defects in a material

ABSTRACT

Described are computer-based methods and apparatuses, including computer program products, for identifying defects in a material. A set of features is identified based on an image of a material, wherein each feature in the set of features is a candidate portion of a defect in the material. A set of chained features is selected based on the set of features, wherein each chained feature comprises one or more features that represent candidate portions of a same defect in the material. A defect in the material is identified based on the set of chained features and the image.

FIELD OF THE INVENTION

The present invention relates generally to computer-based methods andapparatuses, including computer program products, for identifyingdefects in a material.

BACKGROUND

Automated manufacturing processes can be used to quickly and efficientlymanufacture materials in mass quantities. For example, silicon-basedwafers and solar cells can be manufactured using automated fabricationlines. While such manufacturing processes can yield significantquantities of materials, it is often important to inspect the endproduct for quality assurance and/or process control because the revenuerealized by the fabrication plant is often directly related to thequality of the end product (or material). Therefore, a key factor forhigh quality manufacturing processes is often a high speed and highprecision inspection apparatus for testing and screening the end productof the manufacturing process.

For example, for solar cells, the conversion efficiency (how efficientthe PV system is at converting sunlight into electrical energy) oftenhas a direct influence of the output of electrical power. Therefore,solar cell manufacturers want to attain higher conversion efficiencythrough their fabrication lines, because selling prices are related toconversion efficiency. As a result, solar cell manufacturers can employan inspection apparatus to test solar cells for sufficient conversionefficiency.

Manufacturers can use computer vision to inspect fabricated materialsand/or products. However, many manufactured materials do not haveuniform surfaces, which often makes inspection of such materials (e.g.,for defects such as cracks) using computer vision difficult. Forexample, polycrystalline solar cells are often made up of heterogeneoustextures and confusing features. Therefore, defects in polycrystallinesolar cells, such as cracks, often have a non-uniform appearance (e.g.,non-uniform contrast, polarity, width, etc.). Further, portions of suchdefects can often have very low contrast (e.g., 1-2 gray levels).

Present material inspection systems that are configured to detecthard-to-identify defects often use high-resolution images to expose thedefects. However, using high-resolution images is more data-intensive,and therefore requires more time for each inspection.

SUMMARY OF THE INVENTION

One approach to detecting defects of a material using machine vision(e.g., cracks on a polycrystalline solar cell) is to first detect strongfeatures of the defects (e.g., features that can be easily identified inan image of the material with a relatively high degree of certainty,such as large and easily identifiable portions of a crack), and to thenuse the strong defect features to guide the search for weak features ofthe defects (e.g., features that are more difficult to identify, such assmall and thin (e.g., hairline) portions of a crack).

In one aspect, a computerized method is featured for identifying adefect in a material. The method includes generating, by a computingdevice, a preprocessed image based on an original image of a material.The method includes dividing, by the computing device, the preprocessedimage into a set of sub-images. The method includes for a firstsub-image in the set of sub-images, determining, by the computingdevice, whether the first sub-image includes a feature, wherein thefeature is a candidate portion of a defect in the material, and if thefirst sub-image includes the feature, adding, by the computing device,the first sub-image to a set of feature sub-images. The method includesselecting, by the computing device, a chained feature based on the setof feature sub-images, wherein the chained feature comprises one or morefeatures that represent candidate portions of a same defect in thematerial. The method includes identifying, by the computing device, adefect in the material based on the chained feature and the originalimage, comprising calculating a remaining portion of the defect based onthe chained feature.

In another aspect, a computer program product is featured, tangiblyembodied in a non-transitory computer readable medium. The computerprogram product includes instructions being configured to cause a dataprocessing apparatus to generate a preprocessed image based on anoriginal image of a material. The computer program product includesinstructions being configured to cause a data processing apparatus todivide the preprocessed image into a set of sub-images. The computerprogram product includes instructions being configured to cause a dataprocessing apparatus to, for a first sub-image in the set of sub-images,determine whether the first sub-image includes a feature, wherein thefeature is a candidate portion of a defect in the material, and if thefirst sub-image includes the feature, add the first sub-image to a setof feature sub-images. The computer program product includesinstructions being configured to cause a data processing apparatus toselect a chained feature based on the set of feature sub-images, whereinthe chained feature comprises one or more features that representcandidate portions of a same defect in the material. The computerprogram product includes instructions being configured to cause a dataprocessing apparatus to identify a defect in the material based on thechained feature and the original image, comprising calculating aremaining portion of the defect based on the chained feature.

In another aspect, an apparatus is featured for identifying a defect ina material. The apparatus includes a preprocessing module configured togenerate a preprocessed image based on an original image of a material.The apparatus includes a strong feature detection module incommunication with the preprocessing module configured to divide thepreprocessed image into a set of sub-images. The strong featuredetection module is further configured to, for a first sub-image in theset of sub-images, determine whether the first sub-image includes afeature, wherein the feature is a candidate portion of a defect in thematerial, and if the first sub-image includes the feature, add the firstsub-image to a set of feature sub-images. The apparatus includes a weakfeature detection module in communication with the strong featuredetection module configured to select a chained feature based on the setof feature sub-images, wherein the chained feature comprises one or morefeatures that represent candidate portions of a same defect in thematerial. The weak feature detection module is configured to identify adefect in the material based on the chained feature and the originalimage, comprising calculating a remaining portion of the defect based onthe chained feature.

In another aspect, a computerized method is featured for identifying adefect in a solar cell. The method includes identifying, by thecomputing device, a set of features based on an image of a solar cell.The solar cell includes a plurality of textures, and each feature in theset of features is a candidate portion of a defect in the solar cell.The method includes selecting, by the computing device, a set of chainedfeatures based on the set of features, wherein each chained featurecomprises one or more features that represent candidate portions of asame defect in the solar cell. The method includes identifying, by thecomputing device, a defect in the solar cell based on the set of chainedfeatures and the image.

In another aspect, a computer program product is featured, tangiblyembodied in a non-transitory computer readable medium. The computerprogram product includes instructions being configured to cause a dataprocessing apparatus to identify a set of features based on an image ofa solar cell. The solar cell includes a plurality of textures, and eachfeature in the set of features is a candidate portion of a defect in thesolar cell. The computer program product includes instructions beingconfigured to cause a data processing apparatus to select a set ofchained features based on the set of features, wherein each chainedfeature comprises one or more features that represent candidate portionsof a same defect in the solar cell. The computer program productincludes instructions being configured to cause a data processingapparatus to identify a defect in the solar cell based on the set ofchained features and the image.

In another aspect, an apparatus is featured for identifying a defect ina solar cell. The apparatus includes a strong feature detection moduleconfigured to identify a set of features based on an image of a solarcell. The solar cell comprises a plurality of textures, and each featurein the set of features is a candidate portion of a defect in the solarcell. The apparatus includes a weak feature detection module incommunication with the strong feature detection module configured toselect a set of chained features based on the set of features, whereineach chained feature comprises one or more features that representcandidate portions of a same defect in the solar cell. The weak featuredetection module is configured to identify a defect in the solar cellbased on the set of chained features and the image.

In other examples, any of the aspects above can include one or more ofthe following features. Generating the preprocessed image can includegenerating a filtered image, comprising removing one or more featuresusing a filter, and generating the preprocessed image by subtracting thefiltered image from the original image to expose the one or morefeatures in the original image. The preprocessed image can be theoriginal image. The preprocessed image can expose one or more featuresof the material. The preprocessed image can include dark pixels andlight pixels, wherein the dark pixels and the light pixels areidentified based on a grey-level threshold.

In some examples, determining whether the sub-image includes the featureincludes executing a line fitting algorithm using the light pixels, thedark pixels, or both, in the sub-image. It can be determined whether thefeature satisfies a first set of criterions, and it can be determinedwhether the chained features and the calculated remaining portion of thedefect satisfy a second set of criterions.

In other examples, selecting the chained feature includes selecting afeature sub-image from the set of feature sub-images, identifying one ormore sub-images that border the selected feature sub-image, eachidentified sub-image including a feature, and generating a chainedfeature comprising the selected feature sub-image and a sub-image fromthe one or more identified sub-images based on one or more constraints.The one or more constraints can include a position, an orientation, orboth, of the one or more features.

In some examples, identifying the defect includes selecting a pair ofchained features comprising the chained feature, determining if the pairof chained features satisfies a first criterion indicative of the pairof chained features being on a same defect in the material, andcalculating a remaining portion of the same defect between the pair ofthe chained features. The first criterion can be based on a distancebetween the pair of chained features, an end direction of each featurein the pair of chained features, a turning angle of each feature in thepair of chained features, a length of each feature in the pair ofchained features, or any combination thereof.

In other examples, identifying the set of features includes generating apreprocessed image, based on the image, to (a) remove one or moretextures of the plurality of textures of the solar cell in the image, or(b) expose one or more features of a defect in the plurality oftextures, or both. The preprocessed image can be divided into a set ofsub-images. For one or more of the sub-images in the set of sub-images,it can be determined whether the sub-image includes a feature, whereinthe feature is a candidate portion of a defect in the solar cell. If thesub-image includes the feature, the sub-image can be added to a set offeatures.

In some examples, identifying the defect includes calculating aremaining portion of the defect based on pairs of chained features fromthe set of chained features. Identifying the defect can include, foreach pair of chained features from the set of chained features,determining if the pair of chained features satisfies a first criterionindicative of the pair of chained features being on a same defect in thesolar cell, and calculating a remaining portion of the same defectbetween the pair of the chained features, wherein the same defect is aportion of the defect.

In other examples, a representation of the defect is displayed on theimage of the solar cell. Selecting the set of chained features caninclude selecting a sub-image based on the set of features, identifyingone or more sub-images that border the selected sub-image, eachidentified sub-image including a feature, and generating a chainedfeature comprising the selected sub-image and a sub-image from the oneor more identified sub-images based on one or more constraints.

The techniques, which include both computerized methods and apparatuses,described herein can provide one or more of the following advantages.The search for strong features in an image of the material can beconfigured to have a high threshold such that weak features (e.g.,features that can not be easily detected with a high degree ofcertainty) are quickly ruled out. Advantageously, the search for strongfeatures can be performed quickly, and then the strong features can beused to search in a more detailed manner (e.g., with a lower thresholdsuch that weak features can be identified) since the strong features areused as a baseline for the search of weak features. Further, theoriginal image being searched for defects can be filtered and/orprocessed (e.g., to remove weak features and/or other noise) such thatstrong features can be quickly and easily identified. Additionally,separately identified strong features can be joined together to form acontiguous chained strong feature to define a larger portion of thedefect.

Other aspects and advantages of the present invention will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating the principles of theinvention by way of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects, features, and advantages of the presentinvention, as well as the invention itself, will be more fullyunderstood from the following description of various embodiments, whenread together with the accompanying drawings.

FIG. 1 is an exemplary computerized system for detecting defects in amaterial;

FIG. 2A is an exemplary image of a material with two defects;

FIG. 2B is an exemplary preprocessed image generated based on the imageof FIG. 2A;

FIG. 2C is a magnified portion of the processed image of FIG. 2B,showing sub-images for the portion of the image.

FIG. 2D is an exemplary image of the small portions of defects generatedbased on the strong features for the defects and in FIG. 2A.

FIG. 3 is a diagram of an exemplary method for identifying defects in amaterial;

FIG. 4 is a diagram of an exemplary method for identifying a set ofchained features for a portion of a candidate defect in a material.

FIG. 5 is an exemplary diagram of a polycrystalline solar cell with anon-uniform appearance.

DETAILED DESCRIPTION

In general, computerized systems and methods are provided for usingmachine vision to detect and inspect a material (e.g., a solar cell) fordefects (e.g., micro cracks). An original image (e.g., an image of asolar cell) is preprocessed to generate a preprocessed image thatcontains mostly strong features of one or more defects. The strongfeatures of the one or more candidate defects are detected using thepreprocessed image. The strong features and/or the original image arethen used to guide the detection of the weak features of the candidatedefect. The strong features and the weak features are then combined todefine the complete set of actual defects in the material (e.g., a setof strong features are combined with an associated set of weak featuresto define an actual defect).

For example, strong features of a crack (e.g., portions of a crackeasily identifiable using machine vision) are identified using apreprocessed image that exposes the strong features. Detecting thestrong portions of the full crack results in only part of the full crackbeing identified. For example, the detected portions of the crack can bethought of as a dotted line, with the solid portions of the dotted linerepresenting detected strong features, and the white portions of thedotted line representing the undetected weak features. These identifiedstrong features of the crack (e.g., the solid portions of the dottedline) are used to guide detection of the remaining weak features (e.g.,the white portions of the dotted line).

A feature can include, for example, data indicative of a position and anangle, a length (e.g., a line segment), and/or additional information,such as polarity and contrast. The feature can be used (e.g., indecision-making points of the computerized methods described herein) toidentify defects, or portions thereof. The computerized systems andmethods can identify candidate defects by identifying portions of thecandidate defects (e.g., by identifying strong features that may beindicative of a defect). The computerized systems and methods can usethe identified portions to search for remaining portions of thecandidate defects (e.g., weak features), to determine whether thecandidate defect is an actual defect.

Although examples herein are directed to embodiments involving thedetection of cracks in a polycrystalline solar cell, the describedcomputerized systems and methods are not so limited and can be appliedto detecting defects in other types of materials (or products), such assilicon wafers, printed circuit boards, and/or any other type ofmaterial.

FIG. 1 is an exemplary computerized system 100 for detecting defects ina material. The system 100 includes a defect detection computing device102 (defect detection device 102) that is in communication with an imageacquisition device 112 and a display device 114. The defect detectiondevice 102 includes a preprocessing module 104, a strong featuredetection module 106, a weak feature detection module 108, and adatabase 110. The defect detection device 102 can include a processorand memory configured to identify defects in a material.

The system 100 is an example of a computerized system that is speciallyconfigured to perform the computerized methods described herein.However, the system structure and content recited with regard to FIG. 1are for exemplary purposes only and are not intended to limit otherexamples to the specific structure shown in FIG. 1. As will be apparentto one of ordinary skill in the art, many variant system structures canbe architected without departing from the computerized systems andmethods described herein.

In addition, information may flow between the elements, components andsubsystems described herein using any technique. Such techniquesinclude, for example, passing the information over a network usingstandard protocols, such as TCP/IP, passing the information betweenmodules in memory and passing the information by writing to a file,database, or some other non-volatile storage device. In addition,pointers or other references to information may be transmitted andreceived in place of, or in addition to, copies of the information.Conversely, the information may be exchanged in place of, or in additionto, pointers or other references to the information. Other techniquesand protocols for communicating information may be used withoutdeparting from the scope of the invention.

The image acquisition device 112 can be any type of image capturingdevice (e.g., a two dimensional camera, a three dimensional camera, aphotodetector, and/or the like) configured to capture images of thematerials to be inspected by the defect detection device 102. Forexample, the image acquisition device 112 can be located above aconveyor belt of a fabrication process, where the image acquisitiondevice 112 captures images of a representative number of fabricatedmaterials for inspection.

The display device 114 displays images received from the defectdetection device 102 (and/or from the image acquisition device 112). Forexample, the display device 114 can display original images captured bythe image acquisition device 112, preprocessed images generated by thepreprocessing module 104, and/or images with highlighted defects (e.g.,highlighted with lines, colors, text, or other highlighting means). Thedisplay device 114 can be, for example, a computer monitor or atelevision screen. The display device 114 can further include aninterface (e.g., a mouse, keyboard, or other data input device) for anoperator to interface with the defect detection device 102.

The preprocessing module 104 exposes features in received images (e.g.,including weak features and strong features). For example, thepreprocessing module 104 executes a smoothing function (e.g., usingmedian filtering) to remove features and/or noise to generate a filteredimage. The preprocessing module 104 can generate a preprocessed imagebased on the original image and the filtered image (e.g., by subtractingthe filtered image from the original image) to expose features in theoriginal image. The preprocessing module 104 can, in some embodiments,be configured to not perform any preprocessing on original imagesreceived from the image acquisition device 102.

The strong feature detection module 106 extracts strong features ofcandidate portions of defects (or portions of features) based on theoutput of the preprocessing module 104 (e.g., local features based onsub-images). For example, the strong feature detection module 106 cansearch images for candidate portions of defects that can be quicklyand/or easily identified, with a high degree of certainty (or a lownumber of false positives). The weak feature detection module 108detects, based on the extracted strong features, weak features ofdefects that would otherwise be associated with a low degree ofcertainty (e.g., because the weak features have low contrast,non-uniform polarity or width, etc., making detection of the weakfeatures both difficult and time consuming). Advantageously, because thesearch for weak features is guided by the known strong features, therisk of false positives is reduced and a time savings is achieved by notsearching potential weak features if they are not associated with knownstrong features. The preprocessing module 104, strong feature detectionmodule 106 and weak feature detection module 108 is described in furtherdetail below with reference to FIGS. 3 and 4.

FIG. 2A is an exemplary image 200 of a material with two defects 202 and206, respectively (e.g., two cracks in the material). Defect 202includes strong features 204A, 204B and 204C (collectively, strongfeatures 204). For example, the strong features are larger portions ofthe defect that can be easily identified. The remaining portions ofdefect 202 are weak features (e.g., features not as easily identifiedcompared to strong features 204). Defect 206 includes strong features208A, 208B, 208C, 208D, 208E and 208F (collectively, strong features208). The remaining portions of defect 206 are weak features.

FIG. 2B is an exemplary preprocessed image 220 generated based on theimage 200 of FIG. 2A. For example, the preprocessing module 104generates the preprocessed image 220 based on image 200. Preprocessedimage 220 includes the strong features 204 of defect 202, and the strongfeatures 208 of defect 206. The processed image 220 does not include theweak features of either defect 202 or 206. Portion 250 of preprocessedimage 220 is described with further detail with reference to FIG. 2C.

FIG. 2C is a magnified portion 250 of the processed image 220 of FIG.2B, showing sub-images for the portion 250 of processed image 220. Themagnified portion 250 includes sub-images 250A, 250B through 250N(collectively, sub-images 250). Each sub-image represents a portion ofthe entire processed image 220. The size of each sub-image can bepre-configured (e.g., via an operator through the display device 114).For example, each sub-image can be configured to be 50 pixels wide by 50pixels high, 40 pixels wide by 60 pixels high, etc. While FIG. 2C showsthe sub-images as rectangular, non-overlapping, and contiguous blocks,this is for exemplary purposes only. The sub-images can be of any shapeand can be mutually overlapping and/or noncontiguous. Sub-images 252A,252B, 252C, 252D and 252E (collectively, sub images 252) each contain aportion of strong feature 204C. Sub-images 254A, 254B and 254C(collectively, sub images 254) each contain a portion of strong feature204B. For example, sub-image 252C contains feature 262, and sub-image252E contains feature 260. While FIG. 2C shows the features (e.g.,features 260 and 262) mostly as diagonals across the sub-images, this isfor exemplary purposes only. The features can, for example, be at anydirection and/or orientation within the sub-images (e.g., as anygeometric shape, such as straight lines, curved lines, ovals,rectangles, etc.).

FIG. 2D is an exemplary image 280 of the small portions of defectsgenerated based on the strong features 204 and 208 for the defects 202and 206 in FIG. 2A. For defect 202, strong features 204 (e.g., as shownin FIG. 2B) are used to guide detection of weak features 282A and 282B(collectively, weak features 282). For defect 206, strong features 208are used to guide detection of weak features 284A, 284B, 284C, 284D and284E (collectively, weak features 284). Defect 202 is made up of strongfeatures 204 and weak features 282, and defect 206 is made up of strongfeatures 208 and weak features 284 (e.g., detection of the strong andweak features of a defect results in detection of the entire defect, asis explained in further detail below).

FIG. 3 is a diagram of an exemplary method 300 for identifying defectsin a material. Referring to FIGS. 2A-2D, at step 302 the defectdetection device 102 receives an image (e.g., image 200 or image 220) ofa material from the image acquisition device 112. At step 304, thestrong feature detection module 106 identifies (or detects) a set ofstrong features (strong features 204 and 208) for one or more defects202, 204 in the image of the material. At step 306, the weak featuredetection module 108 identifies a set of weak features 282, 284 in theimage based on the identified set of strong features 204, 208. At step308, the defect detection device 102 outputs data indicative of a set ofdefects 202, 206 based on the set of weak features 282, 284 and the setof strong features 204, 208 in the material.

For example, the defect detection device 102 can identify a crack in apolycrystalline solar cell. FIG. 5 is an exemplary diagram of apolycrystalline solar cell 500 with a non-uniform appearance. Thepolycrystalline solar cell 500 includes a non-uniform appearance withfeatures that include different contrast, polarity, width, etc. (e.g.,features 502 and 504, which are not defects in the solar cell 500).Defect detection devices may improperly classify features ofpolycrystalline solar cell 500 as defects (e.g., such as classifyingfeature 502 as a defect). Further, such features like 502, 504 make thesearch for and/or detection of defects more difficult, since oftendefect detection devices are configured to both detect and excludenon-defect features, which results in a large number of features beingdetected and/or analyzed by many defect detection devices.Advantageously, the defect detection device 102 can search for strongfeatures of a defect and then using the strong features to guidedetection of the weak features of the defect. This can allow the defectdetection device 102 to quickly, efficiently, and accurately classifydefects.

Referring to step 304, the method can include identifying a set ofstrong features for a set of any type of defect of the material (e.g.,cracks, chips, manufacturing defects, and/or the like). Advantageously,the strong features of the defects can be identified quickly and with ahigh degree of success (a low chance of false identification of anon-defect feature), and then used to guide the search for weak featuresof the defects that would otherwise be identified slowly and with a lowdegree of success (e.g., since a system would not have any a prioriinformation for the weak defects, the system would need to consider allfeatures that could be weak features, and then classify them one-by-oneas a defect or not).

Referring to step 304, the strong feature detection module 106 candivide the preprocessed image into sub-images 250, and search eachsub-image 250 for portions of strong features. The strong featuredetection module 106 can chain together the identified strong featuresin each sub-image to form a set of chained features. The strong featuredetection module 106 can use the chained features (e.g., which representa complete large defect) to search for weak features.

FIG. 4 is a diagram of an exemplary computerized method 400 foridentifying a set of chained features of a portion of a candidate defectin a material. Referring to FIGS. 2A-2D, at step 402 the preprocessingmodule 104 generates a preprocessed image 220 based on the originalimage 200 of the material, to remove one or more features of thematerial (e.g., to remove noise and/or weak features of the defects fromthe image). At step 404, the strong feature detection module 106 dividesthe preprocessed image 220 into a set of sub-images 250. At step 406,for one or more of the sub-images in the set of sub-images 250, thestrong feature detection module 106 determines whether the sub-imageincludes a feature, wherein the feature is a portion of a defect in thematerial. For example, if the defect in the material is a crack, thestrong feature detection module 106 determines whether the sub-imageincludes a line segment representing a candidate portion of the crack.If the sub-image includes the feature, the strong feature detectionmodule 106 adds the sub-image to a set of feature sub-images (e.g., aset of one or more sub-images, each of which include a strong feature).At step 408, the strong feature detection module 106 calculates a set ofchained features based on the set of feature sub-images. Each chainedfeature includes one or more features that represent portions of a samecrack in the material.

Referring to step 402, the preprocessed image 220 removes backgroundtexture(s) so the strong feature detection module 106 can betteridentify the strong features (e.g., quicker, since the strong featuredetection module 106 analyzes fewer features—only the strong features,and more accurately since the strong features are ofteneasily-identifiable features of the defects). For example,polycrystalline solar cells often have heterogeneous textures, with anappearance as if the solar cell was made by melting together differentpolycrystalline pieces. Therefore, the preprocessed image 220 can removethe edge portions of the different polycrystalline pieces.Advantageously, removing such edge portions and/or other noise, whichare not indicative of defects in the polycrystalline solar cell, allowsthe strong feature detection module 106 to quickly and easily identifystrong features of defects without wasting processing time on non-defect(or potentially non-defect) weak features.

The preprocessing module 104 can generate the preprocessed image basedon a filtered image. For example, the preprocessing module 104 cangenerate a filtered image by executing a filter on the original image200 received from the image acquisition device 112. The filter can be,for example, a filter configured to retain background features and/ortextures (e.g., noise and weak features) and remove strong features fromthe original image 200. For example, the filter can be a low-passfilter, a median filter, and/or a low-frequency smoothing function. Thepreprocessing module 104 can generate the preprocessed image 220 bysubtracting the filtered image (or vice-versus) from the original image200 to remove background textures in the original image, to remove oneor more weak features in the original image, or both. In someembodiments, the preprocessing module 104 can execute a texture filterand use the output of the enhancement filter.

Referring to the preprocessed image 220, while FIG. 2B shows thebackground with white pixels and the strong features with black pixels,the preprocessed image can include a background of dark pixels and a setof strong features comprising light pixels, wherein the dark pixels andthe light pixels are identified based on a grey-level threshold. Forexample, the defect detection device 102 can use a grey-level thresholdto identify which pixels are associated with strong features. Forexample, if the background is black and pixel features are associatedwith lighter pixels, the defect detection device 102 can define agrey-level threshold where all pixels with a grey-level value greaterthan the grey-level threshold are determined to be representative ofpixels potentially associated with strong features of a candidatedefect, while pixels with a grey-level value less than the grey-levelthreshold are determined to not represent pixels potentially associatedwith strong features.

Referring to step 404, the preprocessed image 220 includes a set ofstrong features 204 and 208, and the strong feature detection module 106needs to detect the strong features 204 and 208 and connect themtogether, if necessary, to form complete strong features. The strongfeature detection module 106 divides the preprocessed image 220 into aset of sub-images 250 (e.g., to search for portions of strong featuresin each of the sub-images). In some embodiments, the strong featuredetection module 106 does not actually break up the preprocessed image220 into different data structures, but instead the strong featuredetection module 106 considers certain groups of pixels in an iterativefashion. In some examples, the strong feature detection module 106iteratively considers each sub-image in the preprocessed image 220.Since the strong feature detection module 106 identifies portions offeatures in each sub-image (e.g., portions of a strong feature), thestrong feature detection module 106 can chain together the identifiedset of features in the sub-images to generate a complete feature (e.g.,to generate a complete strong feature), which is described in furtherdetail below.

Referring to step 406, the strong feature detection module 106 generatesa set of feature sub-images, each feature sub-image including a feature(which may be only a portion of a full feature). For example, the strongfeature detection module 106 analyzes one or more sub-images 250 todetermine whether the sub-image includes a line segment (wherein theline segment is a candidate portion of a crack in the material). As anexample, the strong feature detection module 106 determines sub-image252E includes feature 260. The strong feature detection module 106 canadd sub-image 252E to a set of feature sub-images. For example, if thestrong feature detection module 106 analyzes the sub-images 250 of FIG.2C, the set of feature sub-images would include at least sub-images252A, 252C, 252E, 254A and 254C, since each of these sub-images includesa feature within it (e.g., each includes a line segment for a candidateportion of a crack). Further, if the strong feature detection module 106determines sub-image 252B includes a feature (since the upper-leftcorner of sub-image 252B may include a feature), then the strong featuredetection module 106 would add 252B to the feature.

In some embodiments, the strong feature detection module 106 candetermine whether the sub-image includes a line segment by executing aline fitting algorithm. The line-fitting algorithm can be a lightweighttool (e.g., computationally inexpensive to computer processingresources) that takes as input a list of points (e.g., a minimum oftwo-dimensional points). The strong feature detection module 106 cananalyze the pixels of each sub-image to determine which are input to theline fitting algorithm. For example, if the processed image 220 isconfigured such that the background pixels are dark pixels (e.g.,wherein the dark pixels are identified based on a grey-level thresholdvalue) and therefore the strong features are defined by lighter pixels(e.g., wherein the light pixels are identified based on a grey-levelthreshold value), the strong feature detection module 106 can execute aline fitting algorithm using the light pixels in a sub-image. Forexample, the strong feature detection module 106 can use a grey-levelthreshold to determine which pixels to input into the line fittingalgorithm. In some examples, the strong feature detection module 106 canexecute a line fitting algorithm using the dark pixels in the sub-image(and/or a combination of the light pixels and the dark pixels).

In some embodiments, the line fitting algorithm can return either (a) aline segment (e.g., a straight line segment that has a direction and/ora position) or (b) a null value indicative of the sub-image notincluding a line segment. The strong feature detection module 106 canadd a sub-image to the set of feature sub-images (the set of sub imagesthat include line segments) if the line fitting algorithm returns a linesegment. Otherwise, the strong feature detection module 106 can omit thesub-image being analyzed (e.g., and proceed to analyze anothersub-image).

In some embodiments, the strong feature detection module 106 determineswhether a sub-image includes a feature based on a set of constraints(e.g., one or more constraints). The strong feature detection module 106can determine whether the sub-image contains enough feature pixels(e.g., based on a minimum value, after grey-level thresholding pixels inthe sub-image). The strong feature detection module 106 can calculate afeature fit error (e.g., if the feature is a line, whether a line fiterror is below a threshold value). The strong feature detection module106 can determine whether in the original image 200 the candidatefeature in the sub-image has enough contrast in its orientation. Thestrong feature detection module 106 can determine whether in theoriginal image 200 the candidate feature's brightness variation is smallenough in the sub-image. The strong feature detection module 106 candetermine whether in the original image 200 the candidate feature'sthickness is within a thickness range (e.g., if the candidate feature isa line segment for a portion of a crack, it can use a crack thicknessrange).

In some embodiments, other tools besides line detecting tools can beused to detect short lines in the sub-images. For example, the CaliperTool provided by Cognex of Natick, Mass. can be used to detect the twoedges of a line. The Caliper Tool can measure, based on a givendirection and position of the line, how in the image the grey-levelchanges across the line to determine the position of the line. If, forexample, the direction of the line is not known, the Caliper Tool caniteratively test each direction in the sub-image, and using thestrongest two edges to determine where the line is.

Referring to step 408, the strong feature detection module 106determines whether to join together one or more identified features fromstep 406 (e.g., for crack defects, the strong feature detection module106 can chain together neighboring crack features from the varioussub-images to identify a complete defect feature). For example, thestrong feature detection module 106 selects feature sub-image 252E fromthe set of feature sub-images. The strong feature detection module 106identifies one or more sub-images that border the selected featuresub-image, each identified sub-image including a feature. For example,feature sub-image 252E may have eight sub-images that border it (e.g.,the sub-images that are to the top, bottom, left, right, and the fourcorners). Feature sub-image 252C borders sub-image 252E because theupper-right corner of feature sub-image 252C is adjacent to the lowerleft corner of feature sub-image 252E. The strong feature detectionmodule 106 generates a chained feature comprising the feature sub-image252C and feature sub-image 252E (e.g., and therefore features 260 and262 are joined together to form a chained feature). Advantageously, thestrong feature detection module 106 can join together separatelycalculated features to form a contiguous chained feature.

The strong feature detection module 106 can identify chained featuresbased on one or more constraints to ensure that features are chainedtogether into a single chained feature only if they represent candidateportions of a same defect (e.g., of a crack). The one or moreconstraints can include a position, an orientation, or both, of thefeatures the strong feature detection module 106 is analyzing. Forexample, for candidate line segment portions of a crack in adjacentsub-images, the strong feature detection module 106 is configured tochain the line segments together. Advantageously, the strong featuredetection module 106 can chain together separate line segments as longas possible (e.g., into a curved line) to define a large candidateportion of a crack.

The strong feature detection module 106 can start the chaining processfrom a cell in which there is a feature (e.g., a feature sub-image). Thestrong feature detection module 106 can analyze the adjacent cells ofthe feature sub-image (e.g., the eight adjacent sub-images) based onconstraints to potentially chain the feature sub-image together with oneor more neighboring sub-images. For example, a constraint can includeverifying that the direction of the features in the neighboringsub-images are compatible (e.g., verifying that a line is continued fromone line segment to another line segment instead of chaining togethertwo line segments that would be parallel).

The strong feature detection module 106 can analyze neighboringsub-images based on whether or not the neighboring sub-images include afeature. For example, if a selected feature sub-image one has oneneighboring sub-image with a feature, then the strong feature detectionmodule 106 can connect the two features in the respective sub-images.The strong feature detection module 106 can then analyze the neighboringsub-images of the newly chained sub-image (e.g., using the same processused to analyze the initial sub-image.

If, for example, the selected sub-image has multiple neighboringsub-images with features, then the strong feature detection module 106can select which of the multiple sub-images to chain together with theselected sub-image based on a scoring mechanism to tell which is thebest candidate around its neighboring sub-images (e.g., along its foursides and four corners). For example, known information about eachfeature in the sub-images can be used, such as a position of the featurein the sub-image (e.g. the mid-point of the feature) and an angle ofdirection of the feature. The chaining algorithm can use a score tomeasure how well the information of the feature of the current sub-imagematches (or pairs) with the feature(s) in a neighboring sub-image(s).The strong feature detection module 106 can pick the neighboring featurewith the best score to chain with the current feature. The strongfeature detection module 106 can consider as the next feature theselected neighboring feature to look for any additional features tochain together with the neighboring feature. The score can be calculatedbased on, for example, how much a feature has to “turn” in order toconnect with a neighboring feature. For example, since cracks in amaterial are often locally straight (e.g., without many high-curvatureturns), features with a large turn can be disregarded. For example, thefollowing turning angle scoring function can be used:S=|A _(C) −A _(T) |+|A _(T) −A _(N)|  Equation 1Wherein:

-   S=the turning angle score for the current feature (the feature being    analyzed);-   A_(C)=the angle of current feature;-   A_(N)=the angle of a neighboring feature of the current feature; and-   A_(T)=the angle of the turning line segment, which is the line    segment that starts at the mid-point of the current feature and ends    at the mid-point of the neighboring feature.

Regarding A_(C) and A_(T), the angle can be indicative of a direction ofa line in the image coordinate system (e.g., the angle can be measuredby the angle formed by the current feature and the x-axis of thecoordinate system). A low score (S) of 0 means the current feature doesnot have any “turning” when connecting the current feature to theneighboring feature (e.g., the end point of the current feature can beconnected to the beginning point of the neighboring feature using astraight line). A high score (S) means the current feature has a high“turning” angle when connecting the current feature to the neighboringfeature. The strong feature detection module 106 can select theneighboring feature with the smallest score as the next feature toanalyze.

In some embodiments, after the strong feature detection module 106chains together the strong features, there can be a number of disjointchained features (e.g., strong features 204 and 208). However, as shownin original image 200, the strong features 204 are all part of the samedefect 202, and the strong features 208 are all part of the same defect206. The strong feature detection module 106 only detected and chainedtogether the strong features, but the weak features of each defect (weakfeatures 282 and 284) were not yet identified. Advantageously, as isdescribed in FIG. 3, the weak feature detection module 108 uses strongfeatures to guide detection of the weak features (e.g., the weak featuredetection module 108 uses strong features 204 and 208 to guide detectionof weak features 282 and 284, respectively).

Referring to step 306, the weak feature detection module 108 calculatesthe set of weak features for defects based on the identified set ofstrong features from step 304. In some embodiments, the weak featuredetection module 108 identifies a defect (e.g., defect 202 and 206)based on a pair of chained features from the set of chained features.The weak feature detection module 108 can recursively search for pairsof chained features of a defect to use to detect weak features of thesame defect. For example, the weak feature detection module 108 candetermine if a selected pair of chained features satisfies a criterionindicative of the pair of chained features being on a same defect (e.g.,to determine that strong features 204A and 204B are on a same defect202, while strong features 204A and 208A are on different defects 202and 206, respectively). The criterion can be based on, for example, adistance between the pair of chained features (e.g., the pixel distancebetween an end point on strong feature 204A and an end point on strongfeature 204B), an end direction of each feature in the pair of chainedfeatures (e.g., the linear direction of an endpoint on a strongfeature), a turning angle of each feature in the pair of chainedfeatures (e.g., based on Equation 1), a length of each feature in thepair of chained features (e.g., the number of pixels along strongfeature 204A and on strong feature 204B), and/or other criterionindicative of whether the pair of features are part of a same defect.

The weak feature detection module 108 can choose pairs of chainedfeatures (or strong features) using a selection algorithm that scorescandidate pairs of strong features (e.g., and select the highest-scoredpair of strong features). The weak feature detection module 108 canscore pairs based on the distance between the ends of one chainedfeature and the ends of the other chained feature (e.g., the weakfeature detection module 108 may not consider two chained features ifthe endpoints are too far apart). The weak feature detection module 108can score pairs based on the end direction of the chained features(e.g., based on the last two points of each chained feature). Forexample, if the chained feature is a line segment of a portion of acrack, the line segment may be a curve and therefore have no fixeddirection, but the tangential direction of both ends of the line segmentcan be calculated and compared against the tangential direction of theother chained feature being compared with. The weak feature detectionmodule 108 can score pairs based on the turning angle of the chainedfeatures.

The weak feature detection module 108 can choose pairs of chainedfeatures based on the endpoints of each chained feature (referred toherein as “chain ends,” where each chained feature includes a head and atail end). A chain end can include, for example, a single pixel or morethan one pixel (e.g., the chain end can be the entire feature thatbelongs to the sub-image that makes up the end portion of the strongfeature). For a given set of N chained features, there are a total of(2*N) chain ends. To determine which chain end should be paired withanother chain end, the weak feature detection module 108 can execute analgorithm to select the best candidate chain ends.

For example, in some embodiments the algorithm can proceed as follows.Before any chain ends are analyzed, there is an initial pool of (2*N)chain ends. The weak feature detection module 108 selects a chain endand compares the selected chain end with the remaining chain ends todetermine whether one of the chain ends is pairable with the selectedchain end. In some embodiments, the weak feature detection module 108can be configured to not exhaust searching all existing chain endsbefore picking a pair of chain ends. For example, for a given chain end,the weak feature detection module 108 can search for other chain endsand pick a second chain end as soon as the weak feature detection module108 finds a pairable one (which is described in further detail below).In some embodiments, the weak feature detection module 108 is configuredto exhaustively search each potential set of chain ends and to selectthe best pair of chain ends. For example, a unified score could bedesigned to take into account various features of each pair of chainends (e.g., both the chain end distance and collinearity, as discussedfurther below). The weak feature detection module 108 could exhaustivelyevaluate all other chain ends, calculating the unified score for each,and then pick the chain ends with the best score.

The weak feature detection module 108 can determine whether two chainends are pairable based on one or more tests, such as a distance test, aturning angle test, and/or any other test that can provide and/ordetermine information about the pair ends. For example, the weak featuredetection module 108 can execute a distance test that determine whetherthe distance of the end points of the two chain ends is less than amaximum distance threshold (the maximum distance threshold can bepreconfigured (or hardcoded), provided by an operator, etc.). If thedistance is less than the maximum distance threshold, the weak featuredetection module 108 can determine the pair of chain ends satisfies thedistance test, otherwise the weak feature detection module 108 rejectsthe two chain ends as not pairable.

As another example, the weak feature detection module 108 can examinethe collinearity of the two chain ends. For example, the weak featuredetection module 108 can determine how collinear the first few featuresat the two chain ends are (e.g. by measuring the fit error of a straightline fitting the first few feature points, and/or by measuring how farthese features deviate from a line segment that connects the two chainends). As another example, the weak feature detection module 108 candetermine collinearity by executing a turning angle test to determinewhether the turning angle score for the two chain ends is less than amaximum turning angle score threshold. If the turning angle score isless than the maximum turning angle score threshold, the weak featuredetection module 108 can determine the pair of chain ends satisfies theturning angle test, otherwise the weak feature detection module 108rejects the two chain ends as not pairable. The turning angle score canbe calculated, for example, using Equation 1 above. In some examples,the turning angle score is calculated using Equation 2:S=|A _(C) −A _(T) |+|A _(T) −A _(N)|  Equation 2Wherein:

-   S=the turning angle score for the first chain end of the two chain    ends;-   A_(C)=the tangential angle of first chain end;-   A_(N)=the tangential angle of the second chain end of the two chain    ends; and-   A_(T)=the angle of the line segment, which is the line segment that    starts at the end point of the first chain end and ends at the    mid-point of the second chain end.

The weak feature detection module 108 can use a combination of tests todetermine whether two chain ends are pairable. For example, the weakfeature detection module 108 can use both the distance test and thecollinearity test to determine whether two chain ends are pairable(e.g., if the weak feature detection module 108 determines one or moreof the two tests is not satisfied, the weak feature detection module 108rejects the two chain ends as not pairable). Advantageously, the weakfeature detection module 108 can be configured to define pairability as(a) including undetected portions of the defect that are not too large(e.g., weak segments (or portions) of a crack are not too long) and (b)the defects do not include features that are unlikely to define a defect(e.g., the algorithm can be configured to pair portions of a crack thattend to extend, instead of making turns, since cracks with sharp turnsand/or zigzag turns are unlikely).

If a pairable chain end is found, the weak feature detection module 108next determines whether the two chain ends can actually be paired. Ifthe pairing is successful (e.g., if the weak feature detection module108 detects one or more weak features between the paired strongfeatures), the weak feature detection module 108 joins the two chainedfeatures (e.g., with any intermediate features) to form a new chain. Thetwo chain ends (which the weak feature detection module 108 connected)are removed from the pool of chain ends.

The weak feature detection module 108 can repeat the above-describedsteps of the algorithm for a next selected chain end in the pool (e.g.,which now has (2*(N−1)) chain ends). The weak feature detection module108 can be configured to complete the search for pairable chain endswhen there are no chain ends left in the pool, or when the weak featuredetection module 108 determines all remaining chain ends cannot bepaired with any other chain ends.

The weak feature detection module 108 can calculate the remainingportion of the same defect between the pair of the chained features. Forexample, for defect 202, the weak feature detection module 108calculates weak feature 282A based on strong features 204A and 204B, andcalculates weak feature 282B based on strong features 204B and 204C. Forexample, if defect 202 is a crack, the feature detection module 106calculates chained line segments for strong crack features 204, and usesthe strong crack features 204 to guide identification of the weak crackfeatures 282, resulting in identification of the full crack 202.

Advantageously, the strong feature detection module 106 can beconfigured to detect only strong features, and the weak featuredetection module 108 can be configured to use the detected strongfeatures to detect the undetected weak features. For example, the strongfeature detection module 106 may be unable to detect the weak featuresbecause it has certain parameters configured to ensure the moduledetects only defect features and not other features (e.g., and notnon-defect features, such as heterogeneous textures of a solar cell).Therefore, the strong feature detection module 106 can be configured tonot detect weak features and/or noise.

In some embodiments, the weak feature detection module 108 uses theoriginal image 200 to detect weak features (e.g., instead of using thepreprocessed image 220). The weak feature detection module 108 can usethe original image 200 instead of the preprocessed image 220 becauseweak features may have a low contrast in the preprocessed image 220(e.g., due to filtering by using the median filter to alter the pixelsof the original image 200). Advantageously, the strong feature detectionmodule 106 can quickly search for the strong features (e.g., becausesearching the preprocessed image 220 requires processing less data andbecause the strong feature detection module 106 can be configured tostop processing data (e.g., a sub-image) if constraints are not met,allowing a more cursory review of the data to be sufficient). The weakfeature detection module 108 can be configured to perform a moredetailed search of the original image 220 because the strong features ofthe defects have been detected, which are used to guide searches forweak features of the defects (e.g., and therefore, while other weakfeatures may be present in original image 200, the weak featuredetection module 108 may only consider the weak features identifiedbased on the detected strong features).

In some embodiments, the weak feature detection module 108 usesconstraints that are more relaxed than those used by the strong featuredetection module 106 to detect the weak features. In some embodiments,different tools are used by the strong feature detection module 106 andthe weak feature detection module 108 to detect the weak the weakfeatures. For example, the strong feature detection module 106 can usethe Caliper Tool discussed above. The weak feature detection module 108has knowledge of what the direction is in the weak features based on thesurrounding strong features (e.g., strong feature 204C extends into weakfeature 282B). The Caliper Tool can be applied in the expected area(e.g., where weak feature 282B is expected). The Caliper Tool can report(a) if strong enough, the location of the weak feature (e.g., thelocation of the weak feature 282B of the defect 202), and/or (b) nothing(e.g., because there is no match, such as searching for a portion ofdefect 202 below strong feature 204C). The Caliper Tool can detect theweak feature 282B pixel-by-pixel (e.g., by detecting the first pixeladjacent to strong feature 204C, and then iteratively searching for eachnext pixel based on the previous pixel).

In some embodiments, the weak feature detection module can be configuredto search less candidate pixels if the candidate pixels are closer toknown strong features (e.g., at the end points of a weak feature), andto search more candidate pixels if the candidate pixels are further awayfrom the strong features (e.g., at the center of the weak feature). Forexample, a paired set of strong features with a space between the twostrong features can give a hint that the end portions of the weakfeature near the strong features should appear near the adjoining endsof the large features, whereas in the middle of the weak feature, it isless known where that portion of the weak feature is located (e.g., theweak feature may be a curve, so in the middle that portion of the weakfeature may be in an unexpected location).

Regarding step 308 of FIG. 3, the defect detection device 102 assemblesthe detected weak features and strong features together to identifyactual defects. For example, the defect detection device 102 assemblesthe strong features 204 with the weak features 282 to identify defect202 (e.g., a full crack in a solar cell).

The above-described techniques can be implemented in digital and/oranalog electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. The implementation can be as acomputer program product, i.e., a computer program tangibly embodied ina machine-readable storage device, for execution by, or to control theoperation of, a data processing apparatus, e.g., a programmableprocessor, a computer, and/or multiple computers. A computer program canbe written in any form of computer or programming language, includingsource code, compiled code, interpreted code and/or machine code, andthe computer program can be deployed in any form, including as astand-alone program or as a subroutine, element, or other unit suitablefor use in a computing environment. A computer program can be deployedto be executed on one computer or on multiple computers at one or moresites.

Method steps can be performed by one or more processors executing acomputer program to perform functions of the invention by operating oninput data and/or generating output data. Method steps can also beperformed by, and an apparatus can be implemented as, special purposelogic circuitry, e.g., a FPGA (field programmable gate array), a FPAA(field-programmable analog array), a CPLD (complex programmable logicdevice), a PSoC (Programmable System-on-Chip), ASIP(application-specific instruction-set processor), or an ASIC(application-specific integrated circuit). Subroutines can refer toportions of the computer program and/or the processor/special circuitrythat implement one or more functions.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital or analog computer.Generally, a processor receives instructions and data from a read-onlymemory or a random access memory or both. The essential elements of acomputer are a processor for executing instructions and one or morememory devices for storing instructions and/or data. Memory devices,such as a cache, can be used to temporarily store data. Memory devicescan also be used for long-term data storage. Generally, a computer alsoincludes, or is operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,e.g., magnetic, magneto-optical disks, or optical disks. A computer canalso be operatively coupled to a communications network in order toreceive instructions and/or data from the network and/or to transferinstructions and/or data to the network. Computer-readable storagedevices suitable for embodying computer program instructions and datainclude all forms of volatile and non-volatile memory, including by wayof example semiconductor memory devices, e.g., DRAM, SRAM, EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and optical disks,e.g., CD, DVD, HD-DVD, and Blu-ray disks. The processor and the memorycan be supplemented by and/or incorporated in special purpose logiccircuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer in communication with a display device,e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display)monitor, for displaying information to the user and a keyboard and apointing device, e.g., a mouse, a trackball, a touchpad, or a motionsensor, by which the user can provide input to the computer (e.g.,interact with a user interface element). Other kinds of devices can beused to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, and/ortactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributed computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The above describedtechniques can be implemented in a distributed computing system thatincludes any combination of such back-end, middleware, or front-endcomponents.

The computing system can include clients and servers. A client and aserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The components of the computing system can be interconnected by any formor medium of digital or analog data communication (e.g., a communicationnetwork). Examples of communication networks include circuit-based andpacket-based networks. Packet-based networks can include, for example,the Internet, a carrier internet protocol (IP) network (e.g., local areanetwork (LAN), wide area network (WAN), campus area network (CAN),metropolitan area network (MAN), home area network (HAN)), a private IPnetwork, an IP private branch exchange (IPBX), a wireless network (e.g.,radio access network (RAN), 802.11 network, 802.16 network, generalpacket radio service (GPRS) network, HiperLAN), and/or otherpacket-based networks. Circuit-based networks can include, for example,the public switched telephone network (PSTN), a private branch exchange(PBX), a wireless network (e.g., RAN, bluetooth, code-division multipleaccess (CDMA) network, time division multiple access (TDMA) network,global system for mobile communications (GSM) network), and/or othercircuit-based networks.

Devices of the computing system and/or computing devices can include,for example, a computer, a computer with a browser device, a telephone,an IP phone, a mobile device (e.g., cellular phone, personal digitalassistant (PDA) device, laptop computer, electronic mail device), aserver, a rack with one or more processing cards, special purposecircuitry, and/or other communication devices. The browser deviceincludes, for example, a computer (e.g., desktop computer, laptopcomputer) with a world wide web browser (e.g., Microsoft® InternetExplorer® available from Microsoft Corporation, Mozilla® Firefoxavailable from Mozilla Corporation). A mobile computing device includes,for example, a Blackberry®. IP phones include, for example, a Cisco®Unified IP Phone 7985G available from Cisco System, Inc, and/or a Cisco®Unified Wireless Phone 7920 available from Cisco System, Inc.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein. Scope of the invention is thus indicated bythe appended claims, rather than by the foregoing description, and allchanges that come within the meaning and range of equivalency of theclaims are therefore intended to be embraced therein.

What is claimed is:
 1. A computerized method for identifying a defect ina material, comprising: generating, by a computing device, apreprocessed image based on an original image of a material; dividing,by the computing device, the preprocessed image into a set ofsub-images; for a first sub-image in the set of sub-images: determining,by the computing device, whether the first sub-image includes a feature,wherein the feature is a candidate portion of a defect in the material;and if the first sub-image includes the feature, adding, by thecomputing device, the first sub-image to a set of feature sub-images;selecting, by the computing device, a chained feature based on the setof feature sub-images, wherein the chained feature comprises one or morefeatures that represent candidate portions of a same defect in thematerial; and identifying, by the computing device, a defect in thematerial based on the chained feature and the original image, comprisingcalculating a remaining portion of the defect based on the chainedfeature, wherein identifying the defect comprises: selecting a pair ofchained features comprising the chained feature; determining if the pairof chained features satisfies a first criterion indicative of the pairof chained features being on a same defect in the material; andcalculating a remaining portion of the same defect between the pair ofthe chained features.
 2. The method of claim 1, wherein generating thepreprocessed image comprises: generating a filtered image, comprisingremoving one or more features using a filter; and generating thepreprocessed image by subtracting the filtered image from the originalimage to expose the one or more features in the original image.
 3. Themethod of claim 1, wherein the preprocessed image is the original image.4. The method of claim 1, wherein the preprocessed image exposes one ormore features of the material.
 5. The method of claim 1, wherein thepreprocessed image comprises dark pixels and light pixels, wherein thedark pixels and the light pixels are identified based on a grey-levelthreshold.
 6. The method of claim 5, wherein determining whether thesub-image includes the feature comprises executing a line fittingalgorithm using the light pixels, the dark pixels, or both, in thesub-image.
 7. The method of claim 1, further comprising: determining thefeature satisfies a first set of criterions; and determining the chainedfeatures and the calculated remaining portion of the defect satisfy asecond set of criterions.
 8. The method of claim 1, wherein selectingthe chained feature comprises: selecting a feature sub-image from theset of feature sub-images; identifying one or more sub-images thatborder the selected feature sub-image, each identified sub-imageincluding a feature; and generating a chained feature comprising theselected feature sub-image and a sub-image from the one or moreidentified sub-images based on one or more constraints.
 9. The method ofclaim 8, wherein the one or more constraints comprises a position, anorientation, or both, of the one or more features.
 10. The method ofclaim 1, wherein the first criterion is based on a distance between thepair of chained features, an end direction of each feature in the pairof chained features, a turning angle of each feature in the pair ofchained features, a length of each feature in the pair of chainedfeatures, or any combination thereof.
 11. A computer program product,tangibly embodied in a non-transitory computer readable medium, thecomputer program product including instructions being configured tocause a data processing apparatus to: generate a preprocessed imagebased on an original image of a material; divide the preprocessed imageinto a set of sub-images; for a first sub-image in the set ofsub-images: determine whether the first sub-image includes a feature,wherein the feature is a candidate portion of a defect in the material;and if the first sub-image includes the feature, add the first sub-imageto a set of feature sub-images; select a chained feature based on theset of feature sub-images, wherein the chained feature comprises one ormore features that represent candidate portions of a same defect in thematerial; and identify a defect in the material based on the chainedfeature and the original image, comprising calculating a remainingportion of the defect based on the chained feature, wherein identifyingthe defect comprises: selecting a pair of chained features comprisingthe chained feature; determining if the pair of chained featuressatisfies a first criterion indicative of the pair of chained featuresbeing on a same defect in the material; and calculating a remainingportion of the same defect between the pair of the chained features. 12.An apparatus for identifying a defect in a material, comprising: apreprocessing module configured to generate a preprocessed image basedon an original image of a material; a strong feature detection module incommunication with the preprocessing module configured to: divide thepreprocessed image into a set of sub-images; and for a first sub-imagein the set of sub-images: determine whether the first sub-image includesa feature, wherein the feature is a candidate portion of a defect in thematerial; and if the first sub-image includes the feature, add the firstsub-image to a set of feature sub-images; and a weak feature detectionmodule in communication with the strong feature detection moduleconfigured to: select a chained feature based on the set of featuresub-images, wherein the chained feature comprises one or more featuresthat represent candidate portions of a same defect in the material; andidentify a defect in the material based on the chained feature and theoriginal image, comprising calculating a remaining portion of the defectbased on the chained feature, wherein identifying the defect comprises:selecting a pair of chained features comprising the chained feature;determining if the pair of chained features satisfies a first criterionindicative of the pair of chained features being on a same defect in thematerial; and calculating a remaining portion of the same defect betweenthe pair of the chained features.